activity
20142026
most citedANI-1: An extensible neural network potential with DFT accuracy at force field computational cost

2.1k citations · 6k across the 29 of their papers we have counts for

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Showing 2018Show all

2 papers · 1 filter

physics.chem-ph2018★ 2 cited

Transferable Molecular Charge Assignment Using Deep Neural Networks

Ben Nebgen, Nick Lubbers, Justin S. Smith +6

We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molec…

physics.comp-ph2018★ 872 cited

Less is more: sampling chemical space with active learning

Justin S. Smith, Ben Nebgen, Nicholas Lubbers +2

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train suc…